Impact of measurement backaction on nuclear spin qubits in silicon

Phosphorus donor nuclear spins in silicon couple weakly to the environment making them promising candidates for high-fidelity qubits. The state of a donor nuclear spin qubit can be manipulated and read out using its hyperfine interaction with the electron confined by the donor potential. Here we use a master equation-based approach to investigate how the backaction from this electron-mediated measurement affects the lifetimes of single and multi-donor qubits. We analyze this process as a function of electric and magnetic fields, and hyperfine interaction strength. Apart from single nuclear spin flips, we identify an additional measurement-related mechanism, the nuclear spin flip-flop, which is specific to multi-donor qubits. Although this flip-flop mechanism reduces qubit lifetimes, we show that it can be effectively suppressed by the hyperfine Stark shift. We show that using atomic precision donor placement and engineered Stark shift, we can minimize the measurement backaction in multi-donor qubits, achieving larger nuclear spin lifetimes than single donor qubits

Assessing Trustworthy AI in times of COVID-19. Deep Learning for predicting a multi-regional score conveying the degree of lung compromise in COVID-19 patients

Abstract—The paper's main contributions are twofold: to demonstrate how to apply the general European Union’s High-Level Expert Group’s (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare; and to investigate the research question of what does “trustworthy AI” mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multi-regional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient’s lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia (Italy) since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses socio-technical scenarios to identify ethical, technical and domain-specific issues in the use of the AI system in the context of the pandemic.</p

Physical activity, sedentary time and physical capability in early old age: British birth cohort study.

PURPOSE: To investigate the associations of time spent sedentary, in moderate-to-vigorous-intensity physical activity (MVPA) and physical activity energy expenditure (PAEE) with physical capability measures at age 60-64 years. METHODS: Time spent sedentary and in MVPA and, PAEE were assessed using individually calibrated combined heart rate and movement sensing among 1727 participants from the MRC National Survey of Health and Development in England, Scotland and Wales as part of a detailed clinical assessment undertaken in 2006-2010. Multivariable linear regression models were used to examine the cross-sectional associations between standardised measures of each of these behavioural variables with grip strength, chair rise and timed up-&-go (TUG) speed and standing balance time. RESULTS: Greater time spent in MVPA was associated with higher levels of physical capability; adjusted mean differences in each capability measure per 1 standard deviation increase in MVPA time were: grip strength (0.477 kg, 95% confidence interval (CI): 0.015 to 0.939), chair rise speed (0.429 stands/min, 95% CI: 0.093 to 0.764), standing balance time (0.028 s, 95% CI: 0.003 to 0.053) and TUG speed (0.019 m/s, 95% CI: 0.011 to 0.026). In contrast, time spent sedentary was associated with lower grip strength (-0.540 kg, 95% CI: -1.013 to -0.066) and TUG speed (-0.011 m/s, 95% CI: -0.019 to -0.004). Associations for PAEE were similar to those for MVPA. CONCLUSION: Higher levels of MVPA and overall physical activity (PAEE) are associated with greater levels of physical capability whereas time spent sedentary is associated with lower levels of capability. Future intervention studies in older adults should focus on both the promotion of physical activity and reduction in time spent sedentary.This work was supported by the UK Medical Research Council (U120063239, U123092720, MC_UU_12019/1, MC_UU_12019/4, MC_UU_12015/3, and MC_UU_12015/4).This is the final published version. It first appeared at http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0126465

On Assessing Trustworthy AI in Healthcare. Machine Learning as a Supportive Tool to Recognize Cardiac Arrest in Emergency Calls

Artificial Intelligence (AI) has the potential to greatly improve the delivery of healthcare and other services that advance population health and wellbeing. However, the use of AI in healthcare also brings potential risks that may cause unintended harm. To guide future developments in AI, the High-Level Expert Group on AI set up by the European Commission (EC), recently published ethics guidelines for what it terms “trustworthy” AI. These guidelines are aimed at a variety of stakeholders, especially guiding practitioners toward more ethical and more robust applications of AI. In line with efforts of the EC, AI ethics scholarship focuses increasingly on converting abstract principles into actionable recommendations. However, the interpretation, relevance, and implementation of trustworthy AI depend on the domain and the context in which the AI system is used. The main contribution of this paper is to demonstrate how to use the general AI HLEG trustworthy AI guidelines in practice in the healthcare domain. To this end, we present a best practice of assessing the use of machine learning as a supportive tool to recognize cardiac arrest in emergency calls. The AI system under assessment is currently in use in the city of Copenhagen in Denmark. The assessment is accomplished by an independent team composed of philosophers, policy makers, social scientists, technical, legal, and medical experts. By leveraging an interdisciplinary team, we aim to expose the complex trade-offs and the necessity for such thorough human review when tackling socio-technical applications of AI in healthcare. For the assessment, we use a process to assess trustworthy AI, called 1Z-Inspection® to identify specific challenges and potential ethical trade-offs when we consider AI in practice.</jats:p

Analysis of arterial intimal hyperplasia: review and hypothesis

which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background: Despite a prodigious investment of funds, we cannot treat or prevent arteriosclerosis and restenosis, particularly its major pathology, arterial intimal hyperplasia. A cornerstone question lies behind all approaches to the disease: what causes the pathology? Hypothesis: I argue that the question itself is misplaced because it implies that intimal hyperplasia is a novel pathological phenomenon caused by new mechanisms. A simple inquiry into arterial morphology shows the opposite is true. The normal multi-layer cellular organization of the tunica intima is identical to that of diseased hyperplasia; it is the standard arterial system design in all placentals at least as large as rabbits, including humans. Formed initially as one-layer endothelium lining, this phenotype can either be maintained or differentiate into a normal multi-layer cellular lining, so striking in its resemblance to diseased hyperplasia that we have to name it &quot;benign intimal hyperplasia&quot;. However, normal or &quot;benign &quot; intimal hyperplasia, although microscopically identical to pathology, is a controllable phenotype that rarely compromises blood supply. It is remarkable that each human heart has coronary arteries in which a single-layer endothelium differentiates earl

Track E Implementation Science, Health Systems and Economics

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138412/1/jia218443.pd

Implementing an internet-based capacity building program for interdisciplinary midwifery-lead teams in Ethiopia, Kenya Malawi and Somalia

The Swedish care model MIDWIZE defined as midwife-led interdisciplinary care and zero separation between mother and newborn, was implemented in 2020–21 in Ethiopia, Kenya, Malawi, and Somalia in a capacity building programme funded by the Swedish Institute. Objective: To determine the feasibility of using an internet-based capacity building programme contributing to effective midwifery practices in the labour rooms through implementation of dynamic birthing positions, delayed umbilical cord clamping and skin-to-skin care of newborns in the immediate postnatal period. Methods: The design is inspired by process evaluation. Focus group discussions with policy leaders, academicians, and clinicians who participated in the capacity building programme were carried out. Before and after the intervention, the numbers for dynamic birthing positions, delayed umbilical cord clamping and skin-to-skin care of the newborn in the immediate postnatal period were detected. Results: Participants believed the internet-based programme was appropriate for their countries’ contexts based on their need for improved leadership and collaboration, the need for strengthened human resources, and the vast need for improved outcomes of maternal and newborn health. Conclusion: The findings provide insight into the feasibility to expand similar online capacity building programmes in collaboration with onsite policy leaders, academicians, and clinicians in sub-Saharan African countries with an agenda for improvements in maternal and child health. © 202